• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.9
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• pp.416-424
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• 2007

The Bio-Inspired system is a computing model that emulates the objects in ecosystem which are evolving themselves and cooperate each other to perform some tasks. Since it could be used to solved the complex problems that have been very difficult to resolve with previous algorithms, there have been a lot of researches to develop an application based on the Bio-Inspired system. However, since this computing model requires the process of evolving and cooperating with a lot of objects and this process takes a lot of times, it has been very hard to develop an application based on this computing model. This paper presents a parallel simulator for a Bio-Inspired system that is designed and implemented with OMNeT++ on PC clusters, and proves its usefulness by showing its simulation performance for a couple of applications. In the proposed parallel simulator, the functions required in the ERS platform for evolving and cooperating between objects (called Ecogent) are mapped onto the functions of OMNeT++, and they are simulated on PC clusters simultaneously to reduce the total simulation time. The simulation results could be monitored with a GUI In realtime, and they are also recorded into DBMS for systematic analyses afterward. This paper shows the usefulness of the proposed system by analyzing its performances for simulating various applications based on Bio-Inspired system on PC clusters with 4 PCs.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.1_2
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• pp.80-89
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• 2001

The development in the mobile computing systems have arisen new and previously unforeseen problems, such as problems in information management of mobile host, disconnection of mobile host and low bandwidths of wireless communications. Especially, location information management strategy of mobile host results in an increased overhead in mobile computing systems. Due to the mobility of the mobiles host, the changes in the mobile host's address depends on the mobile host's location, and is maintained by mapping physical address on virtual address, Since previously suggested several strategies for mapping method between physical address and virtual address did not tackle the increase of mobile host and distribution of location information, it was not able to support the scalability in mobile computing systems. Thus, to distribute the location inrormation, we propose an advanced n-depth LiST (Location information Search Tree) and the parallel location search and update strategy based on the advanced n-depth LiST. The advanced n-depth LiST is logically a hierarchical structure that clusters the location information server by ring structure and reduces the location information search and update cost by parallel seatch and updated method. The experiment shows that even though the distance of two MHs that communicate with each other is large, due to the strnctural distribution of location information, advanced n-depth LiST results in good performance. Moreover, despite the reduction in the location information search cost, there was no increase in the location information update cost.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1129-1139
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• 2015

Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.1-9
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• 2010

Today's growth of the mobile communication infrastructure made mobile computing systems like cellular phones came next to or surpassed the desktop PCs in popularity due to their mobility. Although the performance of mobile devices is now being improved continuously, it is a current common sense that compute intensive large-scale applications can hardly run on any kind of mobile handset devices. To clear up this problem, we decided to exploit the mobile cluster computing system and surveyed the existing ones first. We found out, however, that most of them are not the actual implementations but a mobile cluster infrastructure proposal or idea suggestions for reliable mobile clustering. To make cell phones participated in cluster computing nodes, in this paper, we propose a redesigned JPVM cluster computing engine and a set of WIPI mobile runtime functions interfacing with it. And we also show the performance evaluation results of real parallel applications running on our Mobile-JPVM cluster computing systems. We find out by the performance evaluation that large-scale applications can sufficiently run on mobile devices such as cellular phones when using our mobile cluster computing engine.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.656-662
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• 2018

Pattern matching algorithm is widely used in many application fields such as bio-informatics, intrusion detection, etc. Among many string matching algorithms, KMP (Knuth-Morris-Pratt) algorithm is commonly used because of its fast execution time when using large texts. However, the processing speed of KMP algorithm is also limited when the text size increases significantly. In this paper, we propose a high throughput parallel KMP algorithm considering CPU-GPU memory hierarchy based on OpenCL in GPGPU (General Purpose computing on Graphic Processing Unit). We focus on the optimization for the allocation of work-times and work-groups, the local memory copy of the pattern data and the failure table, and the overlapping of the data transfer with the string matching operations. The experimental results show that the execution time of the optimized parallel KMP algorithm is about 3.6 times faster than that of the non-optimized parallel KMP algorithm.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.41-47
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• 2013

GP-GPUs are general purposed GPUs for numerical computation based on multiple threads which are originally for graphic processing. GP-GPUs provide cache memory in a form of shared memory which user programs can access directly, unlikely typical cache memory. In this research, we implemented the parallel block LU decomposition program to utilize cache memory in GP-GPUs. The parallel blocked LU decomposition program designed with Nvidia CUDA C run 7~8 times faster than nun-blocked LU decomposition program in the same GP-GPU computation environment.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.8
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• pp.1024-1033
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• 1997

The past several years have witnessed an ever-increasing acceptance and adoption of parallel processing, both for high performance scientific computing as well as for more general purpose applications. Furthermore with increasing needs to perform the complex flow calculations in an efficient manner, the use of the message passing model on distributed networks has emerged as an important alternative to the expensive supercomputers. This work attempts to provide a generic framework to enable the parallelization of all CFD-related works using the master-slave model. This framework consists of (1) input geometry, (2) domain decomposition, (3) grid generation, (4) flow computations, (5) flow visualization, and (6) output display as the sequential components, but performs computations for (2) to (5) in parallel on the workstation clustering. The flow computations are parallized by having multiple copies of the flow-code to solve a PDE on different spatial regions on different processors, while their flow data are exchanged across the region boundaries, and the solution is time-stepped. The Parallel Virtual Machine (PVM) is used for distributed communication in this work.

• Bulletin of the Korean Mathematical Society
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• v.57 no.3
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• pp.649-660
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• 2020

This paper proves a new regularity criterion for solutions to the Cauchy problem of the 3D Boussinesq equations via one directional derivative of the horizontal component of the velocity field (i.e., (∂_iu₁; ∂_ju₂; 0) where i, j ∈ {1, 2, 3}) in the framework of the anisotropic Lebesgue spaces. More precisely, for 0 < T < ∞, if $$\large{\normalsize\displaystyle\smashmargin{2}{\int\nolimits_o}^T}({\HUGE\left\|{\small{\parallel}{\partial}_iu_1(t){\parallel}_{L^{\alpha}_{x_i}}}\right\|}{\small^{\gamma}_{L^{\beta}_{x_{\hat{i}}x_{\bar{i}}}}+}{\HUGE\left\|{\small{\parallel}{\partial}_iu_2(t){\parallel}_{L^{\alpha}_{x_j}}}\right\|}{\small^{\gamma}_{L^{\beta}_{x_{\hat{i}}x_{\bar{i}}}}})dt<{{\infty}},$$ where ${\frac{2}{{\gamma}}}+{\frac{1}{{\alpha}}}+{\frac{2}{{\beta}}}=m{\in}[1,{\frac{3}{2}})$ and ${\frac{3}{m}}{\leq}{\alpha}{\leq}{\beta}<{\frac{1}{m-1}}$, then the corresponding solution (u, θ) to the 3D Boussinesq equations is regular on [0, T]. Here, (i, ${\hat{i}}$, ${\tilde{i}}$) and (j, ${\hat{j}}$, ${\tilde{j}}$) belong to the permutation group on the set 𝕊₃ := {1, 2, 3}. This result reveals that the horizontal component of the velocity field plays a dominant role in regularity theory of the Boussinesq equations.